Channel
@MLinHS
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
42subscribers
-2 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001999119969 |
|---|---|
| Type | Channel |
| Username | @MLinHS |
| Created | Between 1 November 2023 and 30 April 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 10 August 2026 |
| Last confirmed live | 28 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 28 August 2026 |
| On Telegram | t.me/MLinHS |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 28 Aug 2026, 13:54 | 42 | -1 |
| 21 Aug 2026, 16:14 | 43 | -1 |
| 10 Aug 2026, 16:15 | 44 | no change |
| 7 Aug 2026, 22:09 | 44 | first reading |
20 posts held, back to 21 December 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 22 June 2026. An engagement rate over an empty window would be a number about nothing.
Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
3 reactions across 2 posts, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 3 | 100.0% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 2 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 3reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 21 December 2025 to 22 June 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
𝑨𝒅𝒗𝒂𝒏𝒄𝒊𝒏𝒈 𝑨𝒖𝒕𝒐𝒎𝒂𝒕𝒆𝒅 𝑫𝒊𝒂𝒈𝒏𝒐𝒔𝒕𝒊𝒄𝒔 𝒊𝒏 𝑶𝒑𝒉𝒕𝒉𝒂𝒍𝒎𝒐𝒍𝒐𝒈𝒚 Early detection of retinal disease is the key to preventing irreversible vision loss for the 2.2 billion people suffering from vision impairment globally. We are pleased to highlight a significant new study published in the latest MLHS issue. In their paper, authors Foma Molchanov and Sukhwant Pal evaluate the DenseNet201 architecture for multi-class retinal disease cl…
Remote robotic surgery is no longer science fiction. 🤖🏥 A new SAIMSARA video breaks down 3 key signals from a review of 207 original studies and >4,300 participants/sample observations. The evidence suggests that 5G telesurgery can be clinically feasible across distances >1,700 km — but only when latency, redundancy, cybersecurity, and haptic feedback are treated as clinical safety infrastructure. Watch: https://y…
🅐🅘 🅒🅗🅐🅣🅑🅞🅣 🅐🅓🅓🅘🅒🅣🅘🅞🅝, attachment, and emotional dependency. Rare topic, high impact. ☸️SAIMSARA Digital found only 20 original studies with 6k+ participants — but the signal matters: loneliness, perceived empathy, parasocial bonds, and overreliance. Read the evidence map + vote on AI trust: https://doi.org/10.62487/saimsarae9e6a55a #SAIMSARA #DigitalHealth #AIChatbots #AIEthics #MentalHealth
Scientific papers were written for humans. But AI agents do not need another PDF. They need structured, citation-linked evidence they can immediately reason with. SAIMSARA turns scientific literature into machine-readable evidence objects — so your LLM, RAG system, or research agent can transform the evidence into the format you actually need. Not just a paper. 🅔🅥🅘🅓🅔🅝🅒🅔 as 🅙🅢🅞🅝
We are launching the SAIMSARA 🅴🆅🅸🅳🅴🅽🅲🅴 API — built to give life-science and medical AI systems a stronger, faster, and more traceable evidence layer. For many workflows, the problem is no longer: “𝑊ℎ𝑖𝑐ℎ 𝑚𝑜𝑑𝑒𝑙 𝑖𝑠 𝑠𝑚𝑎𝑟𝑡𝑒𝑠𝑡?” The real question is: “𝑊ℎ𝑎𝑡 𝑒𝑣𝑖𝑑𝑒𝑛𝑐𝑒 𝑖𝑠 𝑡ℎ𝑒 𝑚𝑜𝑑𝑒𝑙 𝑎𝑙𝑙𝑜𝑤𝑒𝑑 𝑡𝑜 𝑡ℎ𝑖𝑛𝑘 𝑤𝑖𝑡ℎ?” The SAIMSARA database gives external AI/RAG systems access to large-scale scoping-review evidence objects: searchable, c…
🔥2
SAIMSARA has integrated World ID proof-of-human verification into its editorial workflow, now combining: 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗛𝘂𝗺𝗮𝗻 𝗩𝗲𝗿𝗶𝗳𝗶𝗲𝗱 𝗯𝘆 𝗪𝗼𝗿𝗹𝗱 𝗜𝗗 For us, World ID represents an important layer for the next stage of AI-native publishing: proof that behind machine-generated science, there is still a verified human responsible for editorial review, interpretation, and oversight. #SAIMSARA #WorldID #Machin
Not every LLM is best for every task. At SAIMSARA, one scientific review is built through a multi-model pipeline, where each model is chosen for what it does best. Gemini Flash-Lite helps us with fast first-pass filtering and scientific session building. Grok supports evidence mapping and direction-based vote counting. ChatGPT and Gemini Pro help analyze the session and turn selected evidence into the backbone of t…
Machine Generated Science Journal · ISSN 3054-3991 https://t.me/saimsara_mlhs
The deeper we audit the literature while building ☸️SAIMSARA, the clearer one simple truth becomes: journal prestige is a poor proxy for evidentiary integrity. High-impact platforms provide visibility, speed, and citations, but they do not reliably protect against duplicate publications, abstract inflation, recycled datasets, or the artificial expansion of the evidence base. That is why the future should not belong …
𝐒𝐀𝐈𝐌𝐒𝐀𝐑𝐀 𝐉𝐨𝐮𝐫𝐧𝐚𝐥 𝐫𝐞𝐜𝐞𝐢𝐯𝐞𝐬 𝐈𝐒𝐒𝐍 𝐚𝐧𝐝 𝐛𝐞𝐜𝐚𝐦𝐞𝐬 𝐚𝐧 𝐀𝐈-𝐧𝐚𝐭𝐢𝐯𝐞 𝐬𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐣𝐨𝐮𝐫𝐧𝐚𝐥! From static reviews → to dynamic, interactive evidence ecosystems. 𝐓𝐰𝐨 𝐥𝐚𝐲𝐞𝐫𝐬: 1️⃣ 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 - Synthesizes evidence from 200M+ papers within hours, with a reference depth far beyond typical human-written reviews. 2️⃣ 𝐉𝐨𝐮𝐫𝐧𝐚𝐥 - Builds curated domain issues covering entire research fields at scale. Each issue is paired with its own AI agent.
🎥 Watch the story behind Retract — how it was built and how it screens research papers for retraction risk: https://youtu.be/XnbchLW0QZ4
New MLHS app: ⓇⒺⓉⓇⒶⒸⓉ — trained on real retracted papers. Screen your paper’s retraction risk (text-pattern signal): mlhs.ink/retract Try your own abstract and share your score. #MLHS #ResearchIntegrity #NLP #BERT #Cardiology
Showing the 12 most recent of 20 posts we hold for @MLinHS. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 28 August 2026 — this entry's latest reading, not the date you are reading this.
“ML in Health Science” (@MLinHS), 42 subscribers as measured 28 August 2026. Telegram Register, tgregister.com/channel/MLinHS.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.